Example usage for org.apache.commons.math3.stat.regression SimpleRegression getIntercept

Introduction

In this page you can find the example usage for org.apache.commons.math3.stat.regression SimpleRegression getIntercept.

Prototype

public double getIntercept() 

Source Link

Document

Returns the intercept of the estimated regression line, if #hasIntercept() is true; otherwise 0.

Usage

From source file:com.facebook.presto.operator.aggregation.TestDoubleRegrInterceptAggregation.java

@Override
public Object getExpectedValue(int start, int length) {
    if (length <= 1) {
        return null;
    }/*from w w  w.j av a2 s  .com*/
    SimpleRegression regression = new SimpleRegression();
    for (int i = start; i < start + length; i++) {
        regression.addData(i + 2, i);
    }
    return regression.getIntercept();
}

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From source file:com.facebook.presto.operator.aggregation.TestDoubleRegrInterceptAggregation.java

private void testNonTrivialAggregation(Double[] y, Double[] x) {
    SimpleRegression regression = new SimpleRegression();
    for (int i = 0; i < x.length; i++) {
        regression.addData(x[i], y[i]);/*w w w . j  av  a2  s .c  o  m*/
    }
    double expected = regression.getIntercept();
    checkArgument(Double.isFinite(expected) && expected != 0., "Expected result is trivial");
    testAggregation(expected, createDoublesBlock(y), createDoublesBlock(x));
}

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From source file:com.facebook.presto.operator.aggregation.TestFloatRegrInterceptAggregation.java

@Override
public Object getExpectedValue(int start, int length) {
    if (length <= 1) {
        return null;
    }/*w  w w .j av  a 2  s  .c  o  m*/
    SimpleRegression regression = new SimpleRegression();
    for (int i = start; i < start + length; i++) {
        regression.addData(i + 2, i);
    }
    return (float) regression.getIntercept();
}

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From source file:com.facebook.presto.operator.aggregation.TestFloatRegrInterceptAggregation.java

private void testNonTrivialAggregation(Float[] y, Float[] x) {
    SimpleRegression regression = new SimpleRegression();
    for (int i = 0; i < x.length; i++) {
        regression.addData(x[i], y[i]);//from  ww w  .  j a  v  a 2 s. c  o m
    }
    float expected = (float) regression.getIntercept();
    checkArgument(Float.isFinite(expected) && expected != 0.f, "Expected result is trivial");
    testAggregation(expected, createFloatsBlock(y), createFloatsBlock(x));
}

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From source file:com.facebook.presto.operator.aggregation.TestRealRegrInterceptAggregation.java

private void testNonTrivialAggregation(Float[] y, Float[] x) {
    SimpleRegression regression = new SimpleRegression();
    for (int i = 0; i < x.length; i++) {
        regression.addData(x[i], y[i]);/* w  ww  .j  a va  2  s  .com*/
    }
    float expected = (float) regression.getIntercept();
    checkArgument(Float.isFinite(expected) && expected != 0.f, "Expected result is trivial");
    testAggregation(expected, createBlockOfReals(y), createBlockOfReals(x));
}

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From source file:de.qaware.chronix.timeseries.RegressionTest.java

@Test
public void testRegression() {

    SimpleRegression regression = new SimpleRegression();
    regression.addData(0.0, 1.0);//w ww  .j  a  v a 2s . c om
    regression.addData(1.0, 2.5);
    regression.addData(2.0, 3.0);

    double slope = regression.getSlope();
    double intercept = regression.getIntercept();
    long n = regression.getN();
    double err = regression.getMeanSquareError();
}

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From source file:cn.edu.pku.cbi.mosaichunter.filter.ExomeParameterFilter.java

@Override
public void close() throws IOException {
    Collections.sort(sites, new Comparator<SimpleSite>() {
        public int compare(SimpleSite a, SimpleSite b) {
            if (a.depth > b.depth) {
                return 1;
            } else if (a.depth < b.depth) {
                return -1;
            } else {
                return 0;
            }/*ww  w .  ja  v  a 2 s. com*/
        }
    });

    int n = sites.size();
    if (n == 0) {
        System.out.println("No data.");
        return;
    }
    int[] depth = new int[n];
    long totalDepth = 0;
    double[] af = new double[n];
    for (int i = 0; i < n; ++i) {
        depth[i] = sites.get(i).depth;
        af[i] = sites.get(i).altAf;
        totalDepth += depth[i];
    }

    int maxGroups = n / minGroupSize + 2;
    int m = 0;
    double afMeanAll = 0;

    int[] groupSize = new int[maxGroups];
    int[] groupPos = new int[maxGroups];
    double[] afMean = new double[maxGroups];
    double[] depthMid = new double[maxGroups];
    double[] depthMidR = new double[maxGroups];
    double[] afSd = new double[maxGroups];
    double[] afSd2 = new double[maxGroups];
    for (int i = 0; i <= n; ++i) {
        if (i == n || (i > 0 && depth[i] != depth[i - 1] && groupSize[m] >= minGroupSize)) {
            afMean[m] /= groupSize[m];
            for (int j = groupPos[m]; j < i; ++j) {
                afSd2[m] += (af[j] - afMean[m]) * (af[j] - afMean[m]);
            }
            if (groupSize[m] > 1) {
                afSd2[m] /= groupSize[m] - 1;
            }
            afSd[m] = Math.sqrt(afSd2[m]);
            depthMid[m] = depth[(i + groupPos[m]) / 2];
            depthMidR[m] = 1.0 / depthMid[m];
            m++;
            if (i == n) {
                break;
            }
            groupPos[m] = i;
        }
        afMeanAll += af[i];
        groupSize[m]++;
        afMean[m] += af[i];
    }
    afMeanAll /= n;

    SimpleRegression sr = new SimpleRegression();
    for (int i = 0; i < m; ++i) {
        sr.addData(depthMidR[i], afSd2[i]);
    }

    double k = sr.getSlope();
    double d = sr.getIntercept();

    double v = k / optimalDepth + d - afMeanAll * (1 - afMeanAll) / optimalDepth;
    double alpha = ((1 - afMeanAll) / v - 1 / afMeanAll) * afMeanAll * afMeanAll;
    double beta = alpha * (1 / afMeanAll - 1);

    long averageDepth = n == 0 ? 0 : totalDepth / n;
    System.out.println("average depth: " + averageDepth);
    System.out.println("alpha: " + Math.round(alpha));
    System.out.println("beta: " + Math.round(beta));

    if (rDataWriter != null) {
        rDataWriter.close();
    }
}

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From source file:net.anthonypoon.fintech.assignment.one.part2.Portfolio.java

public Portfolio(String name, List<Stock> stockList, Index index, double riskFreeR) {
    this.name = name;
    // dereference the list
    for (Stock stock : stockList) {
        this.stockList.add(new Stock(stock));
    }/*from  w ww .j av a2 s . c  om*/
    this.riskFreeR = riskFreeR;
    this.refIndex = index;
    this.marketVar = Math.pow(refIndex.getStd(), 2);
    for (Stock stock : this.stockList) {
        stock.setExReturn(stock.getRate() - riskFreeR);
        SimpleRegression regress = new SimpleRegression();
        List<Double> stockRList = stock.getRList();
        List<Double> indexRList = index.getRList();
        for (int i = 0; i < stockRList.size(); i++) {
            regress.addData(indexRList.get(i), stockRList.get(i));
        }
        stock.setAlpha(regress.getIntercept());
        stock.setBeta(regress.getSlope());
        stock.setEK(Math.sqrt(
                Math.pow(stock.getStd(), 2) - Math.pow(regress.getSlope(), 2) * Math.pow(index.getStd(), 2)));
    }
}

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From source file:eu.tango.energymodeller.energypredictor.CpuOnlyEnergyPredictor.java

/**
 * This calculates the mathematical function that predicts the power
 * consumption given the cpu utilisation.
 *
 * @param host The host to get the function for
 * @return The mathematical function that predicts the power consumption
 * given the cpu utilisation.//from  w ww .j a v  a 2 s.  co  m
 */
private PredictorFunction<LinearFunction> retrieveModel(Host host) {
    if (modelCache.containsKey(host)) {
        /**
         * A small cache avoids recalculating the regression so often.
         */
        return modelCache.get(host);
    }
    LinearFunction model = new LinearFunction();
    SimpleRegression regressor = new SimpleRegression(true);
    for (HostEnergyCalibrationData data : host.getCalibrationData()) {
        regressor.addData(data.getCpuUsage(), data.getWattsUsed());
    }
    model.setIntercept(regressor.getIntercept());
    model.setCoefficient(regressor.getSlope());
    PredictorFunction<LinearFunction> answer = new PredictorFunction<>(model, regressor.getSumSquaredErrors(),
            Math.sqrt(regressor.getMeanSquareError()));
    modelCache.put(host, answer);
    return answer;
}

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From source file:edu.jhu.hlt.parma.inference.transducers.UnigramLM.java

/**
 * Does the regression in log-space./* w  w w. j a va  2  s .co  m*/
 * 
 * @param cntCounter
 * @return
 */
// private double[] runLogSpaceRegression(Counter<Integer> cntCounter) {
//    double[] xData = new double[cntCounter.keySet().size()];
//    double[] yData = new double[cntCounter.keySet().size()];
//    double[] wts = new double[cntCounter.keySet().size()];
//    int dataPtr = 0;
//    for(int cnt : cntCounter.keySet()) {
//       xData[dataPtr] = cnt;
//       yData[dataPtr++] = Math.log(cntCounter.getCount(cnt));
//    }
//    Regression reg = new Regression(xData,yData,wts);
//    reg.linear();
//    double[] coeffs = reg.getBestEstimates();
//    assert coeffs.length == 2;

//    return coeffs;
// }

private double[] runLogSpaceRegression(Counter<Integer> cntCounter) {
    SimpleRegression reg = new SimpleRegression();

    for (int cnt : cntCounter.keySet()) {
        reg.addData(cnt, Math.log(cntCounter.getCount(cnt)));
    }

    //System.out.println(reg.getIntercept());
    //System.out.println(reg.getSlope());
    //System.out.println(regression.getSlopeStdErr());

    double[] coeffs = new double[] { reg.getIntercept(), reg.getSlope() };

    return coeffs;
}

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